“School Strike 4 Climate”: Social Media and the International Youth Protest on Climate Change
Bibliographic record
Abstract
Beginning in 2018, youth across the globe participated in protest activities aimed at encouraging government action on climate change. This activism was initiated and led by Swedish teenager, Greta Thunberg. Like other contemporary movements, the School Strike 4 Climate used social media. For this article, we use Twitter trace data to examine the global dynamics of the student strike on March 15, 2019. We offer a nuanced analysis of 993 tweets, employing a combination of qualitative and quantitative analysis. Like other movements, the primary function of these tweets was to share information, but we highlight a unique type of information shared in these tweets—documentation of local events across the globe. We also examine opinions shared about youth, the tactic (protest/strike), and climate change, as well as the assignment of blame on government and other institutions for their inaction and compliance in the climate crisis. This global climate strike reflects a trend in international protest events, which are connected through social media and other digital media tools. More broadly, it allows us to rethink how social media platforms are transforming political engagement by offering actors—especially the younger generation—agency through the ability to voice their concerns to a global audience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".